Overview

Dataset statistics

Number of variables60
Number of observations155250
Missing cells3936413
Missing cells (%)42.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.3 MiB
Average record size in memory488.0 B

Variable types

Numeric23
Categorical17
Unsupported20

Alerts

SURVYEAR has constant value "2024"Constant
MJH has constant value "1.0"Constant
FTPTMAIN has constant value "1.0"Constant
PERMTEMP has constant value "1.0"Constant
SCHOOLN has constant value "1.0"Constant
AGE_12 is highly overall correlated with AGE_6High correlation
AGE_6 is highly overall correlated with AGE_12High correlation
AHRSMAIN is highly overall correlated with ATOTHRS and 5 other fieldsHigh correlation
ATOTHRS is highly overall correlated with AHRSMAIN and 5 other fieldsHigh correlation
COWMAIN is highly overall correlated with UNIONHigh correlation
ESTSIZE is highly overall correlated with FIRMSIZEHigh correlation
FIRMSIZE is highly overall correlated with ESTSIZEHigh correlation
HRSAWAY is highly overall correlated with LFSSTATHigh correlation
LFSSTAT is highly overall correlated with AHRSMAIN and 5 other fieldsHigh correlation
NOC_10 is highly overall correlated with NOC_43 and 1 other fieldsHigh correlation
NOC_43 is highly overall correlated with NOC_10 and 1 other fieldsHigh correlation
PAIDOT is highly overall correlated with XTRAHRSHigh correlation
PAYAWAY is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
SEX is highly overall correlated with NOC_10 and 1 other fieldsHigh correlation
UHRSMAIN is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
UNION is highly overall correlated with COWMAINHigh correlation
UNPAIDOT is highly overall correlated with XTRAHRSHigh correlation
UTOTHRS is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
XTRAHRS is highly overall correlated with PAIDOT and 1 other fieldsHigh correlation
YABSENT is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
YAWAY is highly overall correlated with LFSSTATHigh correlation
LFSSTAT is highly imbalanced (60.5%)Imbalance
AGE_6 has 130476 (84.0%) missing valuesMissing
EVERWORK has 155250 (100.0%) missing valuesMissing
FTPTLAST has 155250 (100.0%) missing valuesMissing
YABSENT has 143133 (92.2%) missing valuesMissing
WKSAWAY has 143133 (92.2%) missing valuesMissing
PAYAWAY has 143133 (92.2%) missing valuesMissing
HRSAWAY has 12117 (7.8%) missing valuesMissing
YAWAY has 134964 (86.9%) missing valuesMissing
PAIDOT has 12117 (7.8%) missing valuesMissing
UNPAIDOT has 12117 (7.8%) missing valuesMissing
XTRAHRS has 12117 (7.8%) missing valuesMissing
WHYPT has 155250 (100.0%) missing valuesMissing
PREVTEN has 155250 (100.0%) missing valuesMissing
DURUNEMP has 155250 (100.0%) missing valuesMissing
FLOWUNEM has 155250 (100.0%) missing valuesMissing
UNEMFTPT has 155250 (100.0%) missing valuesMissing
WHYLEFTO has 155250 (100.0%) missing valuesMissing
WHYLEFTN has 155250 (100.0%) missing valuesMissing
DURJLESS has 155250 (100.0%) missing valuesMissing
AVAILABL has 155250 (100.0%) missing valuesMissing
LKPUBAG has 155250 (100.0%) missing valuesMissing
LKEMPLOY has 155250 (100.0%) missing valuesMissing
LKRELS has 155250 (100.0%) missing valuesMissing
LKATADS has 155250 (100.0%) missing valuesMissing
LKANSADS has 155250 (100.0%) missing valuesMissing
LKOTHERN has 155250 (100.0%) missing valuesMissing
PRIORACT has 155250 (100.0%) missing valuesMissing
YNOLOOK has 155250 (100.0%) missing valuesMissing
TLOLOOK has 155250 (100.0%) missing valuesMissing
AGYOWNK has 88106 (56.8%) missing valuesMissing
EVERWORK is an unsupported type, check if it needs cleaning or further analysisUnsupported
FTPTLAST is an unsupported type, check if it needs cleaning or further analysisUnsupported
WHYPT is an unsupported type, check if it needs cleaning or further analysisUnsupported
PREVTEN is an unsupported type, check if it needs cleaning or further analysisUnsupported
DURUNEMP is an unsupported type, check if it needs cleaning or further analysisUnsupported
FLOWUNEM is an unsupported type, check if it needs cleaning or further analysisUnsupported
UNEMFTPT is an unsupported type, check if it needs cleaning or further analysisUnsupported
WHYLEFTO is an unsupported type, check if it needs cleaning or further analysisUnsupported
WHYLEFTN is an unsupported type, check if it needs cleaning or further analysisUnsupported
DURJLESS is an unsupported type, check if it needs cleaning or further analysisUnsupported
AVAILABL is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKPUBAG is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKEMPLOY is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKRELS is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKATADS is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKANSADS is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKOTHERN is an unsupported type, check if it needs cleaning or further analysisUnsupported
PRIORACT is an unsupported type, check if it needs cleaning or further analysisUnsupported
YNOLOOK is an unsupported type, check if it needs cleaning or further analysisUnsupported
TLOLOOK is an unsupported type, check if it needs cleaning or further analysisUnsupported
CMA has 98075 (63.2%) zerosZeros
AHRSMAIN has 12117 (7.8%) zerosZeros
ATOTHRS has 12117 (7.8%) zerosZeros
HRSAWAY has 122847 (79.1%) zerosZeros
PAIDOT has 128110 (82.5%) zerosZeros
UNPAIDOT has 129012 (83.1%) zerosZeros
XTRAHRS has 114821 (74.0%) zerosZeros

Reproduction

Analysis started2024-06-05 22:29:32.918704
Analysis finished2024-06-05 22:30:51.089509
Duration1 minute and 18.17 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

REC_NUM
Real number (ℝ)

Distinct91422
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55302.465
Minimum2
Maximum112082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:51.189425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5493.9
Q127596.25
median55279.5
Q383006
95-th percentile105101.55
Maximum112082
Range112080
Interquartile range (IQR)55409.75

Descriptive statistics

Standard deviation31965.501
Coefficient of variation (CV)0.57801222
Kurtosis-1.1998815
Mean55302.465
Median Absolute Deviation (MAD)27704.5
Skewness0.0012143056
Sum8.5857076 × 109
Variance1.0217932 × 109
MonotonicityNot monotonic
2024-06-05T18:30:51.327726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77419 4
 
< 0.1%
68245 4
 
< 0.1%
68297 4
 
< 0.1%
37575 4
 
< 0.1%
88417 4
 
< 0.1%
27288 4
 
< 0.1%
13869 4
 
< 0.1%
56186 4
 
< 0.1%
7660 4
 
< 0.1%
19036 4
 
< 0.1%
Other values (91412) 155210
> 99.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
5 3
< 0.1%
7 2
< 0.1%
8 3
< 0.1%
9 2
< 0.1%
10 1
 
< 0.1%
11 3
< 0.1%
12 3
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
112082 1
< 0.1%
112081 1
< 0.1%
112078 1
< 0.1%
112075 1
< 0.1%
112074 1
< 0.1%
112073 1
< 0.1%
112072 1
< 0.1%
112068 1
< 0.1%
112061 1
< 0.1%
112058 1
< 0.1%

SURVYEAR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024
155250 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters621000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 155250
100.0%

Length

2024-06-05T18:30:51.446263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:51.561918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2024 155250
100.0%

Most occurring characters

ValueCountFrequency (%)
2 310500
50.0%
0 155250
25.0%
4 155250
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 621000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 310500
50.0%
0 155250
25.0%
4 155250
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 621000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 310500
50.0%
0 155250
25.0%
4 155250
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 621000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 310500
50.0%
0 155250
25.0%
4 155250
25.0%

SURVMNTH
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
4
39423 
3
38904 
2
38545 
1
38378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters155250
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 39423
25.4%
3 38904
25.1%
2 38545
24.8%
1 38378
24.7%

Length

2024-06-05T18:30:51.649076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:51.766509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 39423
25.4%
3 38904
25.1%
2 38545
24.8%
1 38378
24.7%

Most occurring characters

ValueCountFrequency (%)
4 39423
25.4%
3 38904
25.1%
2 38545
24.8%
1 38378
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155250
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 39423
25.4%
3 38904
25.1%
2 38545
24.8%
1 38378
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common 155250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 39423
25.4%
3 38904
25.1%
2 38545
24.8%
1 38378
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 39423
25.4%
3 38904
25.1%
2 38545
24.8%
1 38378
24.7%

LFSSTAT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
143133 
2
 
12117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters155250
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 143133
92.2%
2 12117
 
7.8%

Length

2024-06-05T18:30:51.870263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:51.978448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 143133
92.2%
2 12117
 
7.8%

Most occurring characters

ValueCountFrequency (%)
1 143133
92.2%
2 12117
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155250
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 143133
92.2%
2 12117
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 155250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 143133
92.2%
2 12117
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 143133
92.2%
2 12117
 
7.8%

PROV
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.541089
Minimum10
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:52.056905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q124
median35
Q347
95-th percentile59
Maximum59
Range49
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.358146
Coefficient of variation (CV)0.41568308
Kurtosis-0.81089136
Mean34.541089
Median Absolute Deviation (MAD)11
Skewness0.0013408306
Sum5362504
Variance206.15636
MonotonicityNot monotonic
2024-06-05T18:30:52.155807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35 50477
32.5%
24 30107
19.4%
59 18112
 
11.7%
48 12139
 
7.8%
46 10720
 
6.9%
47 9534
 
6.1%
13 7406
 
4.8%
12 6877
 
4.4%
10 6717
 
4.3%
11 3161
 
2.0%
ValueCountFrequency (%)
10 6717
 
4.3%
11 3161
 
2.0%
12 6877
 
4.4%
13 7406
 
4.8%
24 30107
19.4%
35 50477
32.5%
46 10720
 
6.9%
47 9534
 
6.1%
48 12139
 
7.8%
59 18112
 
11.7%
ValueCountFrequency (%)
59 18112
 
11.7%
48 12139
 
7.8%
47 9534
 
6.1%
46 10720
 
6.9%
35 50477
32.5%
24 30107
19.4%
13 7406
 
4.8%
12 6877
 
4.4%
11 3161
 
2.0%
10 6717
 
4.3%

CMA
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7870338
Minimum0
Maximum9
Zeros98075
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:52.252081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7979974
Coefficient of variation (CV)1.5657216
Kurtosis0.69006321
Mean1.7870338
Median Absolute Deviation (MAD)0
Skewness1.4007912
Sum277437
Variance7.8287892
MonotonicityNot monotonic
2024-06-05T18:30:52.342340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 98075
63.2%
4 16124
 
10.4%
2 9793
 
6.3%
9 9046
 
5.8%
6 5901
 
3.8%
1 3629
 
2.3%
5 3406
 
2.2%
8 3228
 
2.1%
3 3071
 
2.0%
7 2977
 
1.9%
ValueCountFrequency (%)
0 98075
63.2%
1 3629
 
2.3%
2 9793
 
6.3%
3 3071
 
2.0%
4 16124
 
10.4%
5 3406
 
2.2%
6 5901
 
3.8%
7 2977
 
1.9%
8 3228
 
2.1%
9 9046
 
5.8%
ValueCountFrequency (%)
9 9046
 
5.8%
8 3228
 
2.1%
7 2977
 
1.9%
6 5901
 
3.8%
5 3406
 
2.2%
4 16124
 
10.4%
3 3071
 
2.0%
2 9793
 
6.3%
1 3629
 
2.3%
0 98075
63.2%

AGE_12
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.114628
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:52.434951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3503035
Coefficient of variation (CV)0.38437392
Kurtosis-1.0010237
Mean6.114628
Median Absolute Deviation (MAD)2
Skewness-0.045562259
Sum949296
Variance5.5239266
MonotonicityNot monotonic
2024-06-05T18:30:52.526145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 21129
13.6%
5 20335
13.1%
4 19680
12.7%
7 19529
12.6%
8 19463
12.5%
9 17415
11.2%
3 15476
10.0%
10 12925
8.3%
2 8009
 
5.2%
1 1289
 
0.8%
ValueCountFrequency (%)
1 1289
 
0.8%
2 8009
 
5.2%
3 15476
10.0%
4 19680
12.7%
5 20335
13.1%
6 21129
13.6%
7 19529
12.6%
8 19463
12.5%
9 17415
11.2%
10 12925
8.3%
ValueCountFrequency (%)
10 12925
8.3%
9 17415
11.2%
8 19463
12.5%
7 19529
12.6%
6 21129
13.6%
5 20335
13.1%
4 19680
12.7%
3 15476
10.0%
2 8009
 
5.2%
1 1289
 
0.8%

AGE_6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing130476
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean4.8288125
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:52.612198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2028538
Coefficient of variation (CV)0.24909929
Kurtosis-0.40589834
Mean4.8288125
Median Absolute Deviation (MAD)1
Skewness-0.73001168
Sum119629
Variance1.4468572
MonotonicityNot monotonic
2024-06-05T18:30:52.706343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 9931
 
6.4%
4 5748
 
3.7%
5 5545
 
3.6%
3 2261
 
1.5%
2 1254
 
0.8%
1 35
 
< 0.1%
(Missing) 130476
84.0%
ValueCountFrequency (%)
1 35
 
< 0.1%
2 1254
 
0.8%
3 2261
 
1.5%
4 5748
3.7%
5 5545
3.6%
6 9931
6.4%
ValueCountFrequency (%)
6 9931
6.4%
5 5545
3.6%
4 5748
3.7%
3 2261
 
1.5%
2 1254
 
0.8%
1 35
 
< 0.1%

SEX
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
82603 
2
72647 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters155250
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 82603
53.2%
2 72647
46.8%

Length

2024-06-05T18:30:52.810130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:52.918123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 82603
53.2%
2 72647
46.8%

Most occurring characters

ValueCountFrequency (%)
1 82603
53.2%
2 72647
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155250
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 82603
53.2%
2 72647
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common 155250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 82603
53.2%
2 72647
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 82603
53.2%
2 72647
46.8%

MARSTAT
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6526763
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:53.000219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1119186
Coefficient of variation (CV)0.79614636
Kurtosis-1.2057637
Mean2.6526763
Median Absolute Deviation (MAD)0
Skewness0.7848941
Sum411828
Variance4.4602001
MonotonicityNot monotonic
2024-06-05T18:30:53.096125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 78551
50.6%
6 37764
24.3%
2 27154
 
17.5%
5 6447
 
4.2%
4 4148
 
2.7%
3 1186
 
0.8%
ValueCountFrequency (%)
1 78551
50.6%
2 27154
 
17.5%
3 1186
 
0.8%
4 4148
 
2.7%
5 6447
 
4.2%
6 37764
24.3%
ValueCountFrequency (%)
6 37764
24.3%
5 6447
 
4.2%
4 4148
 
2.7%
3 1186
 
0.8%
2 27154
 
17.5%
1 78551
50.6%

EDUC
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9649404
Minimum0
Maximum6
Zeros1390
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:53.189594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3823131
Coefficient of variation (CV)0.34863402
Kurtosis-0.25265344
Mean3.9649404
Median Absolute Deviation (MAD)1
Skewness-0.59906842
Sum615557
Variance1.9107896
MonotonicityNot monotonic
2024-06-05T18:30:53.278309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 60175
38.8%
5 38093
24.5%
2 25837
16.6%
6 18481
 
11.9%
1 5995
 
3.9%
3 5279
 
3.4%
0 1390
 
0.9%
ValueCountFrequency (%)
0 1390
 
0.9%
1 5995
 
3.9%
2 25837
16.6%
3 5279
 
3.4%
4 60175
38.8%
5 38093
24.5%
6 18481
 
11.9%
ValueCountFrequency (%)
6 18481
 
11.9%
5 38093
24.5%
4 60175
38.8%
3 5279
 
3.4%
2 25837
16.6%
1 5995
 
3.9%
0 1390
 
0.9%

MJH
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
155250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 155250
100.0%

Length

2024-06-05T18:30:53.388769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:53.494678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 155250
100.0%

Most occurring characters

ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155250
50.0%
0 155250
50.0%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

EVERWORK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

FTPTLAST
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

COWMAIN
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2.0
111781 
1.0
43469 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 111781
72.0%
1.0 43469
 
28.0%

Length

2024-06-05T18:30:53.581988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:53.694974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 111781
72.0%
1.0 43469
 
28.0%

Most occurring characters

ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
2 111781
24.0%
1 43469
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155250
50.0%
2 111781
36.0%
1 43469
 
14.0%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
2 111781
24.0%
1 43469
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
2 111781
24.0%
1 43469
 
9.3%

IMMIG
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
3
118990 
2
23796 
1
12464 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters155250
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 118990
76.6%
2 23796
 
15.3%
1 12464
 
8.0%

Length

2024-06-05T18:30:53.789238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:53.903083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 118990
76.6%
2 23796
 
15.3%
1 12464
 
8.0%

Most occurring characters

ValueCountFrequency (%)
3 118990
76.6%
2 23796
 
15.3%
1 12464
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155250
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 118990
76.6%
2 23796
 
15.3%
1 12464
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 155250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 118990
76.6%
2 23796
 
15.3%
1 12464
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 118990
76.6%
2 23796
 
15.3%
1 12464
 
8.0%

NAICS_21
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.977153
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:54.001883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median14
Q317
95-th percentile21
Maximum21
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0088354
Coefficient of variation (CV)0.38597336
Kurtosis-1.0301478
Mean12.977153
Median Absolute Deviation (MAD)4
Skewness-0.11210336
Sum2014703
Variance25.088432
MonotonicityNot monotonic
2024-06-05T18:30:54.110579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
17 22392
14.4%
10 14089
9.1%
21 13021
 
8.4%
14 12744
 
8.2%
16 12002
 
7.7%
6 11561
 
7.4%
12 9340
 
6.0%
7 9283
 
6.0%
11 8521
 
5.5%
8 8228
 
5.3%
Other values (11) 34069
21.9%
ValueCountFrequency (%)
1 1103
 
0.7%
2 485
 
0.3%
3 71
 
< 0.1%
4 3843
 
2.5%
5 1887
 
1.2%
6 11561
7.4%
7 9283
6.0%
8 8228
5.3%
9 6311
4.1%
10 14089
9.1%
ValueCountFrequency (%)
21 13021
8.4%
20 4946
 
3.2%
19 5052
 
3.3%
18 4325
 
2.8%
17 22392
14.4%
16 12002
7.7%
15 4015
 
2.6%
14 12744
8.2%
13 2031
 
1.3%
12 9340
6.0%

NOC_10
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9425958
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:54.220300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7487042
Coefficient of variation (CV)0.55612564
Kurtosis-1.2917635
Mean4.9425958
Median Absolute Deviation (MAD)3
Skewness0.12530434
Sum767338
Variance7.555375
MonotonicityNot monotonic
2024-06-05T18:30:54.311889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 27686
17.8%
8 25876
16.7%
7 25220
16.2%
5 18676
12.0%
1 16173
10.4%
3 15854
10.2%
4 12600
8.1%
10 8256
 
5.3%
9 2963
 
1.9%
6 1946
 
1.3%
ValueCountFrequency (%)
1 16173
10.4%
2 27686
17.8%
3 15854
10.2%
4 12600
8.1%
5 18676
12.0%
6 1946
 
1.3%
7 25220
16.2%
8 25876
16.7%
9 2963
 
1.9%
10 8256
 
5.3%
ValueCountFrequency (%)
10 8256
 
5.3%
9 2963
 
1.9%
8 25876
16.7%
7 25220
16.2%
6 1946
 
1.3%
5 18676
12.0%
4 12600
8.1%
3 15854
10.2%
2 27686
17.8%
1 16173
10.4%

NOC_43
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.365308
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:54.428333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median20
Q334
95-th percentile41
Maximum43
Range42
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.009996
Coefficient of variation (CV)0.60893091
Kurtosis-1.489556
Mean21.365308
Median Absolute Deviation (MAD)13
Skewness0.011603981
Sum3316964
Variance169.26
MonotonicityNot monotonic
2024-06-05T18:30:54.550473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 14035
 
9.0%
2 9036
 
5.8%
33 8669
 
5.6%
8 6973
 
4.5%
34 6950
 
4.5%
9 6763
 
4.4%
20 6712
 
4.3%
11 6608
 
4.3%
36 6486
 
4.2%
7 6177
 
4.0%
Other values (33) 76841
49.5%
ValueCountFrequency (%)
1 526
 
0.3%
2 9036
5.8%
3 3112
 
2.0%
4 3499
 
2.3%
5 4068
2.6%
6 3705
2.4%
7 6177
4.0%
8 6973
4.5%
9 6763
4.4%
10 950
 
0.6%
ValueCountFrequency (%)
43 1166
 
0.8%
42 4873
 
3.1%
41 2217
 
1.4%
40 1389
 
0.9%
39 1574
 
1.0%
38 3799
 
2.4%
37 1556
 
1.0%
36 6486
4.2%
35 14035
9.0%
34 6950
4.5%

YABSENT
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing143133
Missing (%)92.2%
Memory size2.4 MiB
1.0
4277 
3.0
4159 
2.0
2927 
0.0
754 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36351
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 4277
 
2.8%
3.0 4159
 
2.7%
2.0 2927
 
1.9%
0.0 754
 
0.5%
(Missing) 143133
92.2%

Length

2024-06-05T18:30:54.664484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:54.780744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4277
35.3%
3.0 4159
34.3%
2.0 2927
24.2%
0.0 754
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 12871
35.4%
. 12117
33.3%
1 4277
 
11.8%
3 4159
 
11.4%
2 2927
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24234
66.7%
Other Punctuation 12117
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12871
53.1%
1 4277
 
17.6%
3 4159
 
17.2%
2 2927
 
12.1%
Other Punctuation
ValueCountFrequency (%)
. 12117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12871
35.4%
. 12117
33.3%
1 4277
 
11.8%
3 4159
 
11.4%
2 2927
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12871
35.4%
. 12117
33.3%
1 4277
 
11.8%
3 4159
 
11.4%
2 2927
 
8.1%

WKSAWAY
Real number (ℝ)

MISSING 

Distinct99
Distinct (%)0.8%
Missing143133
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean17.331848
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:54.898557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q325
95-th percentile77
Maximum99
Range98
Interquartile range (IQR)24

Descriptive statistics

Standard deviation24.744883
Coefficient of variation (CV)1.4277118
Kurtosis2.9559088
Mean17.331848
Median Absolute Deviation (MAD)3
Skewness1.8736563
Sum210010
Variance612.30923
MonotonicityNot monotonic
2024-06-05T18:30:55.223500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3869
 
2.5%
2 1209
 
0.8%
3 668
 
0.4%
99 467
 
0.3%
4 457
 
0.3%
52 307
 
0.2%
5 271
 
0.2%
6 268
 
0.2%
8 261
 
0.2%
12 230
 
0.1%
Other values (89) 4110
 
2.6%
(Missing) 143133
92.2%
ValueCountFrequency (%)
1 3869
2.5%
2 1209
 
0.8%
3 668
 
0.4%
4 457
 
0.3%
5 271
 
0.2%
6 268
 
0.2%
7 135
 
0.1%
8 261
 
0.2%
9 99
 
0.1%
10 187
 
0.1%
ValueCountFrequency (%)
99 467
0.3%
98 2
 
< 0.1%
97 1
 
< 0.1%
96 5
 
< 0.1%
95 4
 
< 0.1%
94 4
 
< 0.1%
93 4
 
< 0.1%
92 7
 
< 0.1%
91 2
 
< 0.1%
90 8
 
< 0.1%

PAYAWAY
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing143133
Missing (%)92.2%
Memory size2.4 MiB
1.0
6358 
2.0
5759 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36351
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6358
 
4.1%
2.0 5759
 
3.7%
(Missing) 143133
92.2%

Length

2024-06-05T18:30:55.344111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:55.452699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6358
52.5%
2.0 5759
47.5%

Most occurring characters

ValueCountFrequency (%)
. 12117
33.3%
0 12117
33.3%
1 6358
17.5%
2 5759
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24234
66.7%
Other Punctuation 12117
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12117
50.0%
1 6358
26.2%
2 5759
23.8%
Other Punctuation
ValueCountFrequency (%)
. 12117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12117
33.3%
0 12117
33.3%
1 6358
17.5%
2 5759
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12117
33.3%
0 12117
33.3%
1 6358
17.5%
2 5759
15.8%

UHRSMAIN
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.379847
Minimum30
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:55.564171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile32
Q137.5
median40
Q340
95-th percentile47.5
Maximum99
Range69
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.7900774
Coefficient of variation (CV)0.14703149
Kurtosis26.519469
Mean39.379847
Median Absolute Deviation (MAD)0
Skewness4.0384556
Sum6113721.2
Variance33.524996
MonotonicityNot monotonic
2024-06-05T18:30:55.693587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 79182
51.0%
37.5 24842
 
16.0%
35 16285
 
10.5%
30 5246
 
3.4%
32 3449
 
2.2%
50 2755
 
1.8%
45 2503
 
1.6%
36 2355
 
1.5%
44 2113
 
1.4%
42 1914
 
1.2%
Other values (180) 14606
 
9.4%
ValueCountFrequency (%)
30 5246
3.4%
30.2 2
 
< 0.1%
30.5 24
 
< 0.1%
30.8 2
 
< 0.1%
31 207
 
0.1%
31.2 6
 
< 0.1%
31.3 3
 
< 0.1%
31.4 2
 
< 0.1%
31.5 51
 
< 0.1%
31.6 2
 
< 0.1%
ValueCountFrequency (%)
99 47
< 0.1%
98 7
 
< 0.1%
97 2
 
< 0.1%
96 10
 
< 0.1%
92 2
 
< 0.1%
91 4
 
< 0.1%
90 16
 
< 0.1%
88 4
 
< 0.1%
87.5 1
 
< 0.1%
87 5
 
< 0.1%

AHRSMAIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct476
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.217362
Minimum0
Maximum99
Zeros12117
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:55.835120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135
median40
Q340
95-th percentile52
Maximum99
Range99
Interquartile range (IQR)5

Descriptive statistics

Standard deviation13.463762
Coefficient of variation (CV)0.37174882
Kurtosis3.1932983
Mean36.217362
Median Absolute Deviation (MAD)3
Skewness-0.97181394
Sum5622745.5
Variance181.27288
MonotonicityNot monotonic
2024-06-05T18:30:55.967334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 52188
33.6%
37.5 14819
 
9.5%
0 12117
 
7.8%
35 10143
 
6.5%
32 6134
 
4.0%
30 5090
 
3.3%
50 4187
 
2.7%
45 3769
 
2.4%
36 2847
 
1.8%
44 2522
 
1.6%
Other values (466) 41434
26.7%
ValueCountFrequency (%)
0 12117
7.8%
0.1 2
 
< 0.1%
0.5 6
 
< 0.1%
1 13
 
< 0.1%
1.2 1
 
< 0.1%
1.5 2
 
< 0.1%
2 18
 
< 0.1%
2.2 1
 
< 0.1%
2.5 6
 
< 0.1%
3 19
 
< 0.1%
ValueCountFrequency (%)
99 90
0.1%
98 16
 
< 0.1%
97.5 1
 
< 0.1%
97 4
 
< 0.1%
96 17
 
< 0.1%
95 2
 
< 0.1%
94 7
 
< 0.1%
93.5 1
 
< 0.1%
93 1
 
< 0.1%
92 9
 
< 0.1%

FTPTMAIN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
155250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 155250
100.0%

Length

2024-06-05T18:30:56.084866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:56.190738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 155250
100.0%

Most occurring characters

ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155250
50.0%
0 155250
50.0%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

UTOTHRS
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.379847
Minimum30
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:56.293546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile32
Q137.5
median40
Q340
95-th percentile47.5
Maximum99
Range69
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.7900774
Coefficient of variation (CV)0.14703149
Kurtosis26.519469
Mean39.379847
Median Absolute Deviation (MAD)0
Skewness4.0384556
Sum6113721.2
Variance33.524996
MonotonicityNot monotonic
2024-06-05T18:30:56.421229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 79182
51.0%
37.5 24842
 
16.0%
35 16285
 
10.5%
30 5246
 
3.4%
32 3449
 
2.2%
50 2755
 
1.8%
45 2503
 
1.6%
36 2355
 
1.5%
44 2113
 
1.4%
42 1914
 
1.2%
Other values (180) 14606
 
9.4%
ValueCountFrequency (%)
30 5246
3.4%
30.2 2
 
< 0.1%
30.5 24
 
< 0.1%
30.8 2
 
< 0.1%
31 207
 
0.1%
31.2 6
 
< 0.1%
31.3 3
 
< 0.1%
31.4 2
 
< 0.1%
31.5 51
 
< 0.1%
31.6 2
 
< 0.1%
ValueCountFrequency (%)
99 47
< 0.1%
98 7
 
< 0.1%
97 2
 
< 0.1%
96 10
 
< 0.1%
92 2
 
< 0.1%
91 4
 
< 0.1%
90 16
 
< 0.1%
88 4
 
< 0.1%
87.5 1
 
< 0.1%
87 5
 
< 0.1%

ATOTHRS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct476
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.220522
Minimum0
Maximum99
Zeros12117
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:56.557957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135
median40
Q340
95-th percentile52
Maximum99
Range99
Interquartile range (IQR)5

Descriptive statistics

Standard deviation13.467056
Coefficient of variation (CV)0.37180734
Kurtosis3.1948322
Mean36.220522
Median Absolute Deviation (MAD)3
Skewness-0.9697913
Sum5623236
Variance181.36159
MonotonicityNot monotonic
2024-06-05T18:30:56.688263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 52182
33.6%
37.5 14817
 
9.5%
0 12117
 
7.8%
35 10143
 
6.5%
32 6132
 
3.9%
30 5088
 
3.3%
50 4188
 
2.7%
45 3768
 
2.4%
36 2848
 
1.8%
44 2521
 
1.6%
Other values (466) 41446
26.7%
ValueCountFrequency (%)
0 12117
7.8%
0.1 2
 
< 0.1%
0.5 6
 
< 0.1%
1 13
 
< 0.1%
1.2 1
 
< 0.1%
1.5 2
 
< 0.1%
2 18
 
< 0.1%
2.2 1
 
< 0.1%
2.5 6
 
< 0.1%
3 19
 
< 0.1%
ValueCountFrequency (%)
99 91
0.1%
98 16
 
< 0.1%
97.5 1
 
< 0.1%
97 4
 
< 0.1%
96 17
 
< 0.1%
95 2
 
< 0.1%
94 7
 
< 0.1%
93.5 1
 
< 0.1%
93 1
 
< 0.1%
92 9
 
< 0.1%

HRSAWAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct193
Distinct (%)0.1%
Missing12117
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean1.5429021
Minimum0
Maximum99
Zeros122847
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:56.820200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.8317799
Coefficient of variation (CV)3.131618
Kurtosis23.315133
Mean1.5429021
Median Absolute Deviation (MAD)0
Skewness4.2955977
Sum220840.2
Variance23.346097
MonotonicityNot monotonic
2024-06-05T18:30:56.952278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122847
79.1%
8 5228
 
3.4%
7.5 1736
 
1.1%
16 1656
 
1.1%
7 1152
 
0.7%
4 1012
 
0.7%
2 813
 
0.5%
24 777
 
0.5%
15 634
 
0.4%
10 568
 
0.4%
Other values (183) 6710
 
4.3%
(Missing) 12117
 
7.8%
ValueCountFrequency (%)
0 122847
79.1%
0.1 2
 
< 0.1%
0.2 2
 
< 0.1%
0.3 2
 
< 0.1%
0.5 78
 
0.1%
0.7 8
 
< 0.1%
0.8 2
 
< 0.1%
1 436
 
0.3%
1.2 6
 
< 0.1%
1.3 1
 
< 0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
72 1
 
< 0.1%
65 1
 
< 0.1%
60 12
< 0.1%
58 1
 
< 0.1%
56 3
 
< 0.1%
52 1
 
< 0.1%
50 3
 
< 0.1%
48 24
< 0.1%
47 1
 
< 0.1%

YAWAY
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing134964
Missing (%)86.9%
Memory size2.4 MiB
1.0
7591 
3.0
6214 
2.0
4180 
0.0
1956 
4.0
 
345

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60858
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 7591
 
4.9%
3.0 6214
 
4.0%
2.0 4180
 
2.7%
0.0 1956
 
1.3%
4.0 345
 
0.2%
(Missing) 134964
86.9%

Length

2024-06-05T18:30:57.067563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:57.187656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7591
37.4%
3.0 6214
30.6%
2.0 4180
20.6%
0.0 1956
 
9.6%
4.0 345
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 22242
36.5%
. 20286
33.3%
1 7591
 
12.5%
3 6214
 
10.2%
2 4180
 
6.9%
4 345
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40572
66.7%
Other Punctuation 20286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22242
54.8%
1 7591
 
18.7%
3 6214
 
15.3%
2 4180
 
10.3%
4 345
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 20286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22242
36.5%
. 20286
33.3%
1 7591
 
12.5%
3 6214
 
10.2%
2 4180
 
6.9%
4 345
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22242
36.5%
. 20286
33.3%
1 7591
 
12.5%
3 6214
 
10.2%
2 4180
 
6.9%
4 345
 
0.6%

PAIDOT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct193
Distinct (%)0.1%
Missing12117
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean0.88905074
Minimum0
Maximum80
Zeros128110
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:57.313194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6968498
Coefficient of variation (CV)4.1581989
Kurtosis61.333826
Mean0.88905074
Median Absolute Deviation (MAD)0
Skewness6.6605789
Sum127252.5
Variance13.666699
MonotonicityNot monotonic
2024-06-05T18:30:57.446158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128110
82.5%
2 1387
 
0.9%
8 1337
 
0.9%
4 1316
 
0.8%
10 1315
 
0.8%
5 1235
 
0.8%
1 979
 
0.6%
3 903
 
0.6%
12 741
 
0.5%
6 734
 
0.5%
Other values (183) 5076
 
3.3%
(Missing) 12117
 
7.8%
ValueCountFrequency (%)
0 128110
82.5%
0.1 7
 
< 0.1%
0.2 14
 
< 0.1%
0.3 28
 
< 0.1%
0.4 5
 
< 0.1%
0.5 240
 
0.2%
0.6 3
 
< 0.1%
0.7 15
 
< 0.1%
0.8 9
 
< 0.1%
0.9 2
 
< 0.1%
ValueCountFrequency (%)
80 1
 
< 0.1%
75 1
 
< 0.1%
73 1
 
< 0.1%
72 3
< 0.1%
70 5
< 0.1%
69.5 1
 
< 0.1%
68 1
 
< 0.1%
62.5 1
 
< 0.1%
60 5
< 0.1%
58 4
< 0.1%

UNPAIDOT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct109
Distinct (%)0.1%
Missing12117
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean0.75558886
Minimum0
Maximum98
Zeros129012
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:57.590493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0450366
Coefficient of variation (CV)4.0300179
Kurtosis64.318181
Mean0.75558886
Median Absolute Deviation (MAD)0
Skewness6.3598105
Sum108149.7
Variance9.272248
MonotonicityNot monotonic
2024-06-05T18:30:57.724773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129012
83.1%
5 2385
 
1.5%
10 2331
 
1.5%
2 1390
 
0.9%
4 1174
 
0.8%
3 1017
 
0.7%
15 782
 
0.5%
6 714
 
0.5%
1 705
 
0.5%
8 698
 
0.4%
Other values (99) 2925
 
1.9%
(Missing) 12117
 
7.8%
ValueCountFrequency (%)
0 129012
83.1%
0.2 14
 
< 0.1%
0.3 3
 
< 0.1%
0.4 2
 
< 0.1%
0.5 139
 
0.1%
0.6 2
 
< 0.1%
0.7 6
 
< 0.1%
0.8 7
 
< 0.1%
1 705
 
0.5%
1.2 4
 
< 0.1%
ValueCountFrequency (%)
98 1
 
< 0.1%
80 2
< 0.1%
75 2
< 0.1%
65 1
 
< 0.1%
63 1
 
< 0.1%
60 4
< 0.1%
56 1
 
< 0.1%
55 2
< 0.1%
53.5 1
 
< 0.1%
50 2
< 0.1%

XTRAHRS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct212
Distinct (%)0.1%
Missing12117
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean1.6446396
Minimum0
Maximum98
Zeros114821
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:57.863436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7076726
Coefficient of variation (CV)2.8624342
Kurtosis33.161949
Mean1.6446396
Median Absolute Deviation (MAD)0
Skewness4.7005289
Sum235402.2
Variance22.162181
MonotonicityNot monotonic
2024-06-05T18:30:57.998110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114821
74.0%
10 3598
 
2.3%
5 3455
 
2.2%
2 2567
 
1.7%
4 2393
 
1.5%
8 2015
 
1.3%
3 1796
 
1.2%
1 1492
 
1.0%
6 1425
 
0.9%
15 1159
 
0.7%
Other values (202) 8412
 
5.4%
(Missing) 12117
 
7.8%
ValueCountFrequency (%)
0 114821
74.0%
0.1 5
 
< 0.1%
0.2 21
 
< 0.1%
0.3 30
 
< 0.1%
0.4 6
 
< 0.1%
0.5 329
 
0.2%
0.6 1
 
< 0.1%
0.7 18
 
< 0.1%
0.8 16
 
< 0.1%
0.9 2
 
< 0.1%
ValueCountFrequency (%)
98 1
 
< 0.1%
84 1
 
< 0.1%
82 1
 
< 0.1%
80 3
< 0.1%
75 3
< 0.1%
73 1
 
< 0.1%
72 4
< 0.1%
70 6
< 0.1%
69.5 1
 
< 0.1%
68 1
 
< 0.1%

WHYPT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

TENURE
Real number (ℝ)

Distinct240
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.13233
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:58.129406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median73
Q3171
95-th percentile240
Maximum240
Range239
Interquartile range (IQR)145

Descriptive statistics

Standard deviation82.954092
Coefficient of variation (CV)0.8284446
Kurtosis-1.1600251
Mean100.13233
Median Absolute Deviation (MAD)57
Skewness0.55042454
Sum15545545
Variance6881.3814
MonotonicityNot monotonic
2024-06-05T18:30:58.261615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 21683
 
14.0%
6 1878
 
1.2%
7 1875
 
1.2%
8 1855
 
1.2%
9 1734
 
1.1%
5 1734
 
1.1%
10 1657
 
1.1%
18 1627
 
1.0%
11 1603
 
1.0%
19 1597
 
1.0%
Other values (230) 118007
76.0%
ValueCountFrequency (%)
1 1150
0.7%
2 1513
1.0%
3 1492
1.0%
4 1566
1.0%
5 1734
1.1%
6 1878
1.2%
7 1875
1.2%
8 1855
1.2%
9 1734
1.1%
10 1657
1.1%
ValueCountFrequency (%)
240 21683
14.0%
239 210
 
0.1%
238 198
 
0.1%
237 193
 
0.1%
236 241
 
0.2%
235 228
 
0.1%
234 207
 
0.1%
233 183
 
0.1%
232 167
 
0.1%
231 154
 
0.1%

PREVTEN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

HRLYEARN
Real number (ℝ)

Distinct5293
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.209966
Minimum5.77
Maximum208.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:58.391622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.77
5-th percentile17
Q124
median32.79
Q346.15
95-th percentile71.4
Maximum208.33
Range202.56
Interquartile range (IQR)22.15

Descriptive statistics

Standard deviation18.554476
Coefficient of variation (CV)0.49864264
Kurtosis6.974948
Mean37.209966
Median Absolute Deviation (MAD)10.29
Skewness1.9509139
Sum5776847.2
Variance344.26856
MonotonicityNot monotonic
2024-06-05T18:30:58.523843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 4533
 
2.9%
20 3806
 
2.5%
30 3357
 
2.2%
22 2689
 
1.7%
23 2415
 
1.6%
40 2159
 
1.4%
18 2145
 
1.4%
28 2141
 
1.4%
21 2139
 
1.4%
24 2109
 
1.4%
Other values (5283) 127757
82.3%
ValueCountFrequency (%)
5.77 1
 
< 0.1%
6.73 1
 
< 0.1%
6.92 2
 
< 0.1%
7.03 1
 
< 0.1%
7.14 1
 
< 0.1%
7.45 1
 
< 0.1%
7.69 7
< 0.1%
7.81 1
 
< 0.1%
7.93 1
 
< 0.1%
8.08 4
< 0.1%
ValueCountFrequency (%)
208.33 1
 
< 0.1%
206.73 1
 
< 0.1%
205.68 1
 
< 0.1%
205.29 1
 
< 0.1%
205.13 3
 
< 0.1%
200 1
 
< 0.1%
197.8 1
 
< 0.1%
192.31 22
< 0.1%
191.1 2
 
< 0.1%
189.33 1
 
< 0.1%

UNION
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
3.0
101992 
1.0
50187 
2.0
 
3071

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 101992
65.7%
1.0 50187
32.3%
2.0 3071
 
2.0%

Length

2024-06-05T18:30:58.647910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:58.760437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 101992
65.7%
1.0 50187
32.3%
2.0 3071
 
2.0%

Most occurring characters

ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
3 101992
21.9%
1 50187
 
10.8%
2 3071
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155250
50.0%
3 101992
32.8%
1 50187
 
16.2%
2 3071
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
3 101992
21.9%
1 50187
 
10.8%
2 3071
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
3 101992
21.9%
1 50187
 
10.8%
2 3071
 
0.7%

PERMTEMP
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
155250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 155250
100.0%

Length

2024-06-05T18:30:58.858285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:58.963737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 155250
100.0%

Most occurring characters

ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155250
50.0%
0 155250
50.0%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

ESTSIZE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2.0
50198 
1.0
42580 
3.0
35351 
4.0
27121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 50198
32.3%
1.0 42580
27.4%
3.0 35351
22.8%
4.0 27121
17.5%

Length

2024-06-05T18:30:59.053731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:59.170221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 50198
32.3%
1.0 42580
27.4%
3.0 35351
22.8%
4.0 27121
17.5%

Most occurring characters

ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
2 50198
 
10.8%
1 42580
 
9.1%
3 35351
 
7.6%
4 27121
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155250
50.0%
2 50198
 
16.2%
1 42580
 
13.7%
3 35351
 
11.4%
4 27121
 
8.7%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
2 50198
 
10.8%
1 42580
 
9.1%
3 35351
 
7.6%
4 27121
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
2 50198
 
10.8%
1 42580
 
9.1%
3 35351
 
7.6%
4 27121
 
5.8%

FIRMSIZE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
4.0
81634 
3.0
26081 
2.0
24908 
1.0
22627 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 81634
52.6%
3.0 26081
 
16.8%
2.0 24908
 
16.0%
1.0 22627
 
14.6%

Length

2024-06-05T18:30:59.278101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:59.398529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 81634
52.6%
3.0 26081
 
16.8%
2.0 24908
 
16.0%
1.0 22627
 
14.6%

Most occurring characters

ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
4 81634
17.5%
3 26081
 
5.6%
2 24908
 
5.3%
1 22627
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155250
50.0%
4 81634
26.3%
3 26081
 
8.4%
2 24908
 
8.0%
1 22627
 
7.3%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
4 81634
17.5%
3 26081
 
5.6%
2 24908
 
5.3%
1 22627
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 155250
33.3%
0 155250
33.3%
4 81634
17.5%
3 26081
 
5.6%
2 24908
 
5.3%
1 22627
 
4.9%

DURUNEMP
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

FLOWUNEM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

UNEMFTPT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

WHYLEFTO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

WHYLEFTN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

DURJLESS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

AVAILABL
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

LKPUBAG
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

LKEMPLOY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

LKRELS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

LKATADS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

LKANSADS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

LKOTHERN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

PRIORACT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

YNOLOOK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

TLOLOOK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155250
Missing (%)100.0%
Memory size2.4 MiB

SCHOOLN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
155250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465750
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 155250
100.0%

Length

2024-06-05T18:30:59.507731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:30:59.612698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 155250
100.0%

Most occurring characters

ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310500
66.7%
Other Punctuation 155250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155250
50.0%
0 155250
50.0%
Other Punctuation
ValueCountFrequency (%)
. 155250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155250
33.3%
. 155250
33.3%
0 155250
33.3%

EFAMTYPE
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8320644
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:30:59.700349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.9462021
Coefficient of variation (CV)1.0236209
Kurtosis1.7179905
Mean4.8320644
Median Absolute Deviation (MAD)1
Skewness1.7449938
Sum750178
Variance24.464915
MonotonicityNot monotonic
2024-06-05T18:30:59.800282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 45584
29.4%
2 33675
21.7%
1 24769
16.0%
18 11673
 
7.5%
4 10894
 
7.0%
14 5946
 
3.8%
6 5237
 
3.4%
5 4864
 
3.1%
8 4155
 
2.7%
15 2450
 
1.6%
Other values (8) 6003
 
3.9%
ValueCountFrequency (%)
1 24769
16.0%
2 33675
21.7%
3 45584
29.4%
4 10894
 
7.0%
5 4864
 
3.1%
6 5237
 
3.4%
7 1337
 
0.9%
8 4155
 
2.7%
9 1707
 
1.1%
10 887
 
0.6%
ValueCountFrequency (%)
18 11673
7.5%
17 195
 
0.1%
16 152
 
0.1%
15 2450
 
1.6%
14 5946
3.8%
13 135
 
0.1%
12 57
 
< 0.1%
11 1533
 
1.0%
10 887
 
0.6%
9 1707
 
1.1%

AGYOWNK
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing88106
Missing (%)56.8%
Memory size2.4 MiB
1.0
22091 
2.0
20772 
3.0
12900 
4.0
11381 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters201432
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 22091
 
14.2%
2.0 20772
 
13.4%
3.0 12900
 
8.3%
4.0 11381
 
7.3%
(Missing) 88106
56.8%

Length

2024-06-05T18:30:59.921985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:31:00.223765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 22091
32.9%
2.0 20772
30.9%
3.0 12900
19.2%
4.0 11381
17.0%

Most occurring characters

ValueCountFrequency (%)
. 67144
33.3%
0 67144
33.3%
1 22091
 
11.0%
2 20772
 
10.3%
3 12900
 
6.4%
4 11381
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134288
66.7%
Other Punctuation 67144
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67144
50.0%
1 22091
 
16.5%
2 20772
 
15.5%
3 12900
 
9.6%
4 11381
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 67144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 201432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 67144
33.3%
0 67144
33.3%
1 22091
 
11.0%
2 20772
 
10.3%
3 12900
 
6.4%
4 11381
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 67144
33.3%
0 67144
33.3%
1 22091
 
11.0%
2 20772
 
10.3%
3 12900
 
6.4%
4 11381
 
5.7%

FINALWT
Real number (ℝ)

Distinct1975
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.25916
Minimum1
Maximum2795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-05T18:31:00.343543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile63
Q1137
median220
Q3370
95-th percentile965
Maximum2795
Range2794
Interquartile range (IQR)233

Descriptive statistics

Standard deviation289.20274
Coefficient of variation (CV)0.91734923
Kurtosis6.1671367
Mean315.25916
Median Absolute Deviation (MAD)100
Skewness2.2614994
Sum48943984
Variance83638.227
MonotonicityNot monotonic
2024-06-05T18:31:00.465568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 589
 
0.4%
136 582
 
0.4%
131 573
 
0.4%
124 569
 
0.4%
140 562
 
0.4%
141 562
 
0.4%
135 555
 
0.4%
133 555
 
0.4%
130 552
 
0.4%
142 552
 
0.4%
Other values (1965) 149599
96.4%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 5
< 0.1%
8 5
< 0.1%
9 4
< 0.1%
10 6
< 0.1%
ValueCountFrequency (%)
2795 1
< 0.1%
2737 1
< 0.1%
2667 1
< 0.1%
2666 1
< 0.1%
2653 2
< 0.1%
2652 2
< 0.1%
2650 1
< 0.1%
2648 1
< 0.1%
2591 2
< 0.1%
2500 1
< 0.1%

Interactions

2024-06-05T18:30:45.598207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:48.275164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:51.014056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:53.569561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:56.037688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:58.535576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:01.113975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:03.633703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:06.182039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:09.014150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:11.490003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:13.964032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:16.443353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:19.062368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:21.601664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:24.434912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:26.981137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:29.508876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-05T18:29:57.989907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:00.574581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:03.077424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:05.619443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:08.424714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:10.943544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:13.422854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:15.932145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:18.488830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:21.042261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:23.854841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:26.420743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:28.948087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:31.768001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:34.331204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:36.909165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:39.527906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:42.379812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:45.023798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:47.710458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:50.396879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:53.113178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:55.598441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:58.099452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:00.684625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:03.185639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:05.728755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:08.541008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:11.048922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:13.528077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:16.013441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:18.598921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:21.149852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:23.965785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:26.527519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:29.055420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:31.889878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:34.439013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:37.035112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:39.635509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:42.496190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:45.134390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:47.990347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:50.503649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:53.221466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:55.702763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:58.204938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:00.785237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:03.292217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:05.838235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:08.654736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:11.156356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:13.634141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:16.120855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:18.711226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:21.259936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:24.078601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:26.637683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:29.164209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:32.005218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:34.546924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:37.175183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:39.913093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:42.610738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:45.246819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:48.109120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:50.782936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:53.343435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:55.820995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:58.323970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:00.899735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:03.412687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:05.957736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:08.780640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:11.272650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:13.752211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:16.234321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:18.832975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:21.379010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:24.200244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:26.756451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:29.283709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:32.134354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:34.668420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:37.298544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:40.031994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:42.736414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:45.369132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:48.223294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:50.897191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:53.459200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:55.931199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:29:58.434996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:01.006521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:03.525034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:06.072418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:08.899574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:11.384874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:13.864212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:16.341157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:18.950939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:21.493377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:24.319423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:26.871420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:29.399787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:32.259928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:34.782295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:37.417779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:40.145961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:42.858204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-05T18:30:45.486372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-05T18:31:00.623750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AGE_12AGE_6AGYOWNKAHRSMAINATOTHRSCMACOWMAINEDUCEFAMTYPEESTSIZEFINALWTFIRMSIZEHRLYEARNHRSAWAYIMMIGLFSSTATMARSTATNAICS_21NOC_10NOC_43PAIDOTPAYAWAYPROVREC_NUMSEXSURVMNTHTENUREUHRSMAINUNIONUNPAIDOTUTOTHRSWKSAWAYXTRAHRSYABSENTYAWAY
AGE_121.0000.8900.5000.0130.0130.0160.135-0.0350.0070.065-0.1040.0640.143-0.0080.1540.045-0.3270.020-0.067-0.061-0.0260.193-0.0310.0020.0510.0000.478-0.0200.0600.065-0.020-0.0540.0290.2700.072
AGE_60.8901.0000.016-0.007-0.0070.0910.1490.359-0.1870.1000.0280.0790.3550.0310.0700.059-0.3550.078-0.223-0.2380.0130.140-0.005-0.0010.0750.0000.301-0.0090.0650.098-0.0090.1700.0680.1370.073
AGYOWNK0.5000.0161.0000.0750.0750.0460.042-0.0820.3840.0120.0530.0100.001-0.0460.1150.1350.0010.012-0.025-0.018-0.0310.253-0.011-0.0030.0530.0000.276-0.0100.0220.043-0.010-0.2960.0110.3700.084
AHRSMAIN0.013-0.0070.0751.0001.0000.0040.222-0.032-0.0100.0560.0110.0650.076-0.4800.0420.969-0.021-0.1970.0790.0710.3391.0000.0560.0000.2270.024-0.0090.5470.1110.2950.547NaN0.4761.0000.071
ATOTHRS0.013-0.0070.0751.0001.0000.0040.222-0.032-0.0100.0560.0110.0650.076-0.4800.0420.969-0.021-0.1970.0790.0710.3381.0000.0560.0000.2270.024-0.0090.5470.1110.2940.547NaN0.4761.0000.071
CMA0.0160.0910.0460.0040.0041.0000.1120.1500.0420.0760.4940.0370.085-0.0240.2520.022-0.0290.022-0.107-0.112-0.0560.0950.3250.0030.0160.000-0.021-0.0170.0930.030-0.017-0.009-0.0180.0710.078
COWMAIN0.1350.1490.0420.2220.2220.1121.000-0.2340.0040.2970.0920.350-0.242-0.0620.1000.0730.063-0.4960.1660.166-0.0160.1890.0510.0000.1990.000-0.2180.2790.610-0.1050.279-0.073-0.0850.0710.104
EDUC-0.0350.359-0.082-0.032-0.0320.150-0.2341.000-0.0360.1050.0640.1030.4050.0030.1460.016-0.1530.214-0.353-0.385-0.0720.133-0.009-0.0010.1330.000-0.002-0.1450.0680.189-0.1450.0170.0840.1380.071
EFAMTYPE0.007-0.1870.384-0.010-0.0100.0420.004-0.0361.0000.0330.0090.032-0.043-0.0070.0860.039-0.089-0.0060.0190.022-0.0200.0690.011-0.0050.3000.0000.006-0.0050.045-0.020-0.0050.027-0.0290.2150.068
ESTSIZE0.0650.1000.0120.0560.0560.0760.2970.1050.0331.0000.0470.5140.2910.0170.0460.049-0.0600.053-0.104-0.1120.0470.167-0.0370.0010.0310.0000.154-0.0590.1900.062-0.0590.0280.0810.0140.073
FINALWT-0.1040.0280.0530.0110.0110.4940.0920.0640.0090.0471.0000.0100.050-0.0250.0840.0140.050-0.031-0.030-0.034-0.0250.0300.2630.0030.0280.011-0.083-0.0020.0480.014-0.002-0.025-0.0080.0370.022
FIRMSIZE0.0640.0790.0100.0650.0650.0370.3500.1030.0320.5140.0101.0000.2630.0240.0220.056-0.0630.104-0.111-0.1190.0470.193-0.0220.0010.0510.0000.185-0.1030.2310.096-0.1030.0380.1050.0220.084
HRLYEARN0.1430.3550.0010.0760.0760.085-0.2420.405-0.0430.2910.0500.2631.0000.0030.0630.022-0.2030.035-0.325-0.3680.0080.2610.075-0.0010.1160.0000.270-0.0200.1460.242-0.020-0.1190.1850.1050.101
HRSAWAY-0.0080.031-0.046-0.480-0.480-0.024-0.0620.003-0.0070.017-0.0250.0240.0031.0000.0111.0000.0030.039-0.0010.001-0.0240.0000.009-0.0020.0250.0110.021-0.0430.031-0.010-0.043NaN-0.0280.0000.093
IMMIG0.1540.0700.1150.0420.0420.2520.1000.1460.0860.0460.0840.0220.0630.0111.0000.0130.1640.0030.0220.0210.0270.068-0.1500.0030.0210.0000.093-0.0230.0620.043-0.0230.0080.0500.1120.035
LFSSTAT0.0450.0590.1350.9690.9690.0220.0730.0160.0390.0490.0140.0560.0221.0000.0131.000-0.0240.0410.0060.007NaN1.000-0.0090.0030.0830.0350.042-0.0210.077NaN-0.021NaNNaN1.0001.000
MARSTAT-0.327-0.3550.001-0.021-0.021-0.0290.063-0.153-0.089-0.0600.050-0.063-0.2030.0030.164-0.0241.000-0.0100.1050.1110.0090.071-0.0650.0020.0990.001-0.193-0.0180.044-0.056-0.018-0.036-0.0360.1350.060
NAICS_210.0200.0780.012-0.197-0.1970.022-0.4960.214-0.0060.053-0.0310.1040.0350.0390.0030.041-0.0101.000-0.306-0.304-0.0670.170-0.047-0.0010.4020.0000.067-0.3260.3420.061-0.3260.094-0.0110.1670.110
NOC_10-0.067-0.223-0.0250.0790.079-0.1070.166-0.3530.019-0.104-0.030-0.111-0.325-0.0010.0220.0060.105-0.3061.0000.9910.1450.1900.0210.0020.5070.000-0.0700.2070.289-0.1780.207-0.023-0.0200.2040.132
NOC_43-0.061-0.238-0.0180.0710.071-0.1120.166-0.3850.022-0.112-0.034-0.119-0.3680.0010.0210.0070.111-0.3040.9911.0000.1470.1760.0220.0020.5060.000-0.0760.2040.292-0.1940.204-0.017-0.0300.1750.132
PAIDOT-0.0260.013-0.0310.3390.338-0.056-0.016-0.072-0.0200.047-0.0250.0470.008-0.0240.027NaN0.009-0.0670.1450.1471.0000.0000.028-0.0010.0850.0060.0090.0970.053-0.0520.097NaN0.6900.0000.010
PAYAWAY0.1930.1400.2531.0001.0000.0950.1890.1330.0690.1670.0300.1930.2610.0000.0681.0000.0710.1700.1900.1760.0001.000-0.021-0.0150.0930.090-0.194-0.0060.129NaN-0.0060.431NaN0.4290.000
PROV-0.031-0.005-0.0110.0560.0560.3250.051-0.0090.011-0.0370.263-0.0220.0750.009-0.150-0.009-0.065-0.0470.0210.0220.028-0.0211.0000.0010.0270.000-0.0460.0980.0880.0140.098-0.0060.0320.0980.091
REC_NUM0.002-0.001-0.0030.0000.0000.0030.000-0.001-0.0050.0010.0030.001-0.001-0.0020.0030.0030.002-0.0010.0020.002-0.001-0.0150.0011.0000.0000.0170.002-0.0020.0060.006-0.002-0.0110.0050.0190.000
SEX0.0510.0750.0530.2270.2270.0160.1990.1330.3000.0310.0280.0510.1160.0250.0210.0830.0990.4020.5070.5060.0850.0930.0270.0001.0000.0000.039-0.2850.0800.049-0.2850.219-0.0370.3100.087
SURVMNTH0.0000.0000.0000.0240.0240.0000.0000.0000.0000.0000.0110.0000.0000.0110.0000.0350.0010.0000.0000.0000.0060.0900.0000.0170.0001.000-0.0070.0060.0000.0010.006-0.0490.0010.0810.096
TENURE0.4780.3010.276-0.009-0.009-0.021-0.218-0.0020.0060.154-0.0830.1850.2700.0210.0930.042-0.1930.067-0.070-0.0760.009-0.194-0.0460.0020.039-0.0071.000-0.0430.1440.095-0.0430.0170.0760.1740.069
UHRSMAIN-0.020-0.009-0.0100.5470.547-0.0170.279-0.145-0.005-0.059-0.002-0.103-0.020-0.043-0.023-0.021-0.018-0.3260.2070.2040.097-0.0060.098-0.002-0.2850.006-0.0431.0000.089-0.0341.000-0.0850.0540.1980.053
UNION0.0600.0650.0220.1110.1110.0930.6100.0680.0450.1900.0480.2310.1460.0310.0620.0770.0440.3420.2890.2920.0530.1290.0880.0060.0800.0000.1440.0891.0000.0060.161-0.068-0.0750.0520.063
UNPAIDOT0.0650.0980.0430.2950.2940.030-0.1050.189-0.0200.0620.0140.0960.242-0.0100.043NaN-0.0560.061-0.178-0.194-0.052NaN0.0140.0060.0490.0010.095-0.0340.0061.000-0.034NaN0.6660.0000.011
UTOTHRS-0.020-0.009-0.0100.5470.547-0.0170.279-0.145-0.005-0.059-0.002-0.103-0.020-0.043-0.023-0.021-0.018-0.3260.2070.2040.097-0.0060.098-0.002-0.2850.006-0.0431.0000.161-0.0341.000-0.0850.0540.1980.053
WKSAWAY-0.0540.170-0.296NaNNaN-0.009-0.0730.0170.0270.028-0.0250.038-0.119NaN0.008NaN-0.0360.094-0.023-0.017NaN0.431-0.006-0.0110.219-0.0490.017-0.085-0.068NaN-0.0851.000NaN0.3530.000
XTRAHRS0.0290.0680.0110.4760.476-0.018-0.0850.084-0.0290.081-0.0080.1050.185-0.0280.050NaN-0.036-0.011-0.020-0.0300.690NaN0.0320.005-0.0370.0010.0760.054-0.0750.6660.054NaN1.0000.0000.015
YABSENT0.2700.1370.3701.0001.0000.0710.0710.1380.2150.0140.0370.0220.1050.0000.1121.0000.1350.1670.2040.1750.0000.4290.0980.0190.3100.0810.1740.1980.0520.0000.1980.3530.0001.0000.000
YAWAY0.0720.0730.0840.0710.0710.0780.1040.0710.0680.0730.0220.0840.1010.0930.0351.0000.0600.1100.1320.1320.0100.0000.0910.0000.0870.0960.0690.0530.0630.0110.0530.0000.0150.0001.000

Missing values

2024-06-05T18:30:48.492757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-05T18:30:49.348017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-05T18:30:50.860112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

REC_NUMSURVYEARSURVMNTHLFSSTATPROVCMAAGE_12AGE_6SEXMARSTATEDUCMJHEVERWORKFTPTLASTCOWMAINIMMIGNAICS_21NOC_10NOC_43YABSENTWKSAWAYPAYAWAYUHRSMAINAHRSMAINFTPTMAINUTOTHRSATOTHRSHRSAWAYYAWAYPAIDOTUNPAIDOTXTRAHRSWHYPTTENUREPREVTENHRLYEARNUNIONPERMTEMPESTSIZEFIRMSIZEDURUNEMPFLOWUNEMUNEMFTPTWHYLEFTOWHYLEFTNDURJLESSAVAILABLLKPUBAGLKEMPLOYLKRELSLKATADSLKANSADSLKOTHERNPRIORACTYNOLOOKTLOLOOKSCHOOLNEFAMTYPEAGYOWNKFINALWT
452024113506NaN1141.0NaNNaN2.036.08.035.0NaNNaNNaN45.042.01.045.042.00.0NaN0.00.00.0NaN212.0NaN28.003.01.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0231
6720241124210NaN1561.0NaNNaN2.0314.03.013.0NaNNaNNaN37.537.51.037.537.50.0NaN0.00.00.0NaN240.0NaN24.743.01.03.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN159
7820241159036.01241.0NaNNaN2.036.08.035.0NaNNaNNaN40.084.01.040.084.00.0NaN44.00.044.0NaN17.0NaN40.003.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN131
101120241159010NaN1141.0NaNNaN1.0316.03.013.0NaNNaNNaN35.020.01.035.020.015.00.00.00.00.0NaN240.0NaN36.331.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN299
11122024122404NaN2141.0NaNNaN2.037.01.04.00.03.02.040.00.01.040.00.0NaNNaNNaNNaNNaNNaN1.0NaN34.753.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN390
16172024113506NaN2151.0NaNNaN1.0217.04.018.0NaNNaNNaN37.549.51.037.549.50.0NaN12.00.012.0NaN30.0NaN27.001.01.03.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0121
202120241135010NaN1141.0NaNNaN2.037.02.09.0NaNNaNNaN40.040.01.040.040.00.0NaN0.00.00.0NaN19.0NaN23.003.01.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN122
22232024111008NaN1141.0NaNNaN1.0321.08.037.0NaNNaNNaN84.084.01.084.084.00.0NaN0.00.00.0NaN215.0NaN13.101.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.044.034
26272024113535NaN2161.0NaNNaN1.0321.02.06.0NaNNaNNaN37.537.51.037.537.50.0NaN0.00.00.0NaN176.0NaN53.853.01.01.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.031.0487
303120241147036.02641.0NaNNaN2.036.08.035.0NaNNaNNaN40.040.01.040.040.00.0NaN0.00.00.0NaN105.0NaN40.001.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN165
REC_NUMSURVYEARSURVMNTHLFSSTATPROVCMAAGE_12AGE_6SEXMARSTATEDUCMJHEVERWORKFTPTLASTCOWMAINIMMIGNAICS_21NOC_10NOC_43YABSENTWKSAWAYPAYAWAYUHRSMAINAHRSMAINFTPTMAINUTOTHRSATOTHRSHRSAWAYYAWAYPAIDOTUNPAIDOTXTRAHRSWHYPTTENUREPREVTENHRLYEARNUNIONPERMTEMPESTSIZEFIRMSIZEDURUNEMPFLOWUNEMUNEMFTPTWHYLEFTOWHYLEFTNDURJLESSAVAILABLLKPUBAGLKEMPLOYLKRELSLKATADSLKANSADSLKOTHERNPRIORACTYNOLOOKTLOLOOKSCHOOLNEFAMTYPEAGYOWNKFINALWT
4425491120582024411006NaN2141.0NaNNaN1.0317.02.09.0NaNNaNNaN37.522.51.037.522.515.03.00.00.00.0NaN130.0NaN24.691.01.03.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN37
4425521120612024414808NaN1421.0NaNNaN1.0317.07.034.0NaNNaNNaN36.026.01.036.026.06.01.00.00.00.0NaN40.0NaN21.931.01.03.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN682
4425591120682024415994NaN1661.0NaNNaN2.0314.05.019.0NaNNaNNaN55.055.01.055.055.00.0NaN0.00.00.0NaN44.0NaN48.953.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN1528
4425631120722024414668NaN1121.0NaNNaN2.037.02.09.0NaNNaNNaN40.048.01.040.048.00.0NaN8.00.08.0NaN172.0NaN32.641.01.03.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN179
4425641120732024413505NaN1621.0NaNNaN2.031.09.040.0NaNNaNNaN45.050.01.045.050.00.0NaN5.00.05.0NaN57.0NaN25.003.01.01.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN180
4425651120742024412426NaN1241.0NaNNaN2.039.08.038.0NaNNaNNaN40.040.01.040.040.00.0NaN0.00.00.0NaN59.0NaN16.001.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0199
4425661120752024414888NaN1141.0NaNNaN2.0211.08.036.0NaNNaNNaN40.040.01.040.040.00.0NaN0.00.00.0NaN9.0NaN27.003.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.063.0531
4425691120782024413544NaN2151.0NaNNaN2.0314.03.011.0NaNNaNNaN40.040.01.040.040.00.0NaN0.00.00.0NaN20.0NaN38.463.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN329
4425721120812024414887NaN2151.0NaNNaN1.0221.05.022.0NaNNaNNaN37.537.51.037.537.50.0NaN0.00.00.0NaN75.0NaN43.081.01.04.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0753
4425731120822024414709NaN2541.0NaNNaN2.0316.07.031.0NaNNaNNaN40.060.01.040.060.00.0NaN0.00.00.0NaN142.0NaN21.953.01.02.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN165